The main motivation behind our work is to allow radiologists and oncologists to accurately segment tumors and make better treatment plans. In order to achieve the desired goal, we propose a machine learning method that takes as input a 3D image and increases the resolution of the input image by a factor of \(2\times \)  or \(4\times \) , providing as output a high-resolution 3D image. To our knowledge, there are only a few previous works [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]}, [18]} that study the super-resolution of CT or MRI images. Similar to some of these previous works [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [9]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]}, [18]}, we approach single-image super-resolution (SISR) of CT and MRI scans using deep convolutional neural networks (CNNs). We propose a CNN architecture composed of 10 convolutional layers and an intermediate sub-pixel convolutional (upscaling) layer [34]} that is placed after the first 6 convolutional layers. Different from related works [3]}, [6]}, [16]}, [18]} that use the sub-pixel convolutional layer of Shi et al. [34]}, we add 4 convolutional layers after the upscaling layer. In order to obtain 3D super-resolution, we employ two CNNs with similar architectures, as illustrated in Figure REF . The first CNN increases the resolution on two axes (width and height), while the second CNN takes the output from the first CNN and further increases the resolution on the third axis (depth). Different from related methods [3]}, [6]}, [16]}, [18]}, we compute the loss with respect to the ground-truth high-resolution image right after the upscaling layer, in addition to computing the loss after the last convolutional layer. The intermediate loss forces our network to produce a better output, closer to the ground-truth. In order to improve the results and obtain sharper images, a common approach is to apply Gaussian smoothing on top of the input images, using a fixed standard deviation. Different from other medical image super-resolution methods [3]}, [14]}, [17]}, we use various standard deviations in order to avoid overfitting to a certain standard deviation and improve the generalization capacity of our model.
